Brain functional network has been widely applied to investigate brain function changes among different conditions and proved to be a small-world-like network. But seldom researches explore the effects of mental fatigue on the small-world brain functional network organization. In the present study, 20 healthy individuals were included to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) signals were recorded before and after the task. Correlations between all pairs of EEG channels were determined by mutual information (MI). The resulting adjacency matrices were converted into brain functional networks by applying a threshold, and then, the clustering coefficient (C), characteristic path length (L), and corresponding small-world feature were calculated. Through performing analysis of variance (ANOVA) on the mean MI for every EEG rhythm, only the data of α1 rhythm during the task state were emerged for the further explorations of mental fatigue. For a wide range of thresholds, C increased and L and small-world feature decreased with the deepening mental fatigue. The pattern of the small-world characteristic still existed when computed with a constant degree. Our present findings indicated that more functional connectivities were activated at the mental fatigue stage for efficient information transmission and processing, and mental fatigue can be characterized by a reduced small-world network characteristic. Our results provide a new perspective to understand the neural mechanisms of mental fatigue based on complex network theories.
Silicon carbide (SiC) is conceived to be one of the next-generation semiconductor materials owning to its outstanding properties and numerous potential applications. However, polishing the hard-to-process SiC substrate remains as a challenge due to its high Mohs hardness and high chemical stability. New technique should be developed aiming at high polishing efficiency and high surface accuracy for chemical mechanical polishing (CMP). This paper presents an emerging approach by employing femtosecond (fs) laser as a pre-process for CMP process. By irradiating the C-faces in transverse and cross-scanning irradiation modes sequentially, both the irradiated and the non-irradiated substrates were simultaneously polished in CMP process with normal colloidal silica slurry in a short time. The performance of fs-laser irradiation assisted CMP was evaluated and possible effects including polishing efficiency and surface accuracy were investigated. Furthermore, surface analytical techniques XRD and XPS were carried out to analyze the mechanisms for improved polishing behavior. Experimental results indicate that three factors, namely, periodic rippled surface morphology, oxidation effect and an amorphous layer were demonstrated to play significant roles in better polishing performance. This study was a beneficial and significant exploration of expanding the application of high precision fs laser to CMP process.
The maximum eigenvalue of the adjacency matrix (AM) has been supposed to contain rich information about the corresponding network. An experimental study focused on revealing the meaning and application of the maximum eigenvalue is missing. To this end, AM was constructed using mutual information (MI) to determine the functional connectivity with electroencephalogram (EEG) data recorded with a mental fatigue model, and then was converted into both binary and weighted brain functional network (BFN) and corresponding random networks (RNs). Both maximum eigenvalue and corresponding network characters in BFNs and RNs were considered to explore the changes during the formation of mental fatigue. The results indicated that large maximum eigenvalue means more edges in the corresponding network, along with a high degree and a short characteristic path length both in weighted and binary BFNs. Interestingly, the maximum eigenvalue of AM was always a little larger than that of the corresponding random matrix (RM), and had an obvious linearity with the sum of the AM elements, indicating that the maximum eigenvalue can be able to distinguish the network structures which have the same mean degree. What is more, the maximum eigenvalue, which increased with the deepening of mental fatigue, can become a good indicator for mental fatigue estimation.
The Taihang Mountains are an important ecological barrier in China, and their ecosystems have good carbon sink capacity. Studying the spatial-temporal variation characteristics and driving factors of carbon storage in the Taihang Mountains ecosystem provides decision-making for the construction of “dual carbon” projects and the improvement of ecological environment quality in this region. This paper takes the area in the Taihang Mountains as the research area, based on the land use and carbon density data of 2005, 2010, 2015, and 2019 of the Taihang Mountains, calculates the carbon storage in the region with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, explores the main factors affecting the spatial differentiation of carbon storage in this region, and analyzes their driving mechanisms by Geodetector. The results show that: (1) From 2005 to 2019, the land use of the Taihang Mountains changed somewhat. The area of forest and construction land increased slightly, while the area of farmland and grassland decreased. (2) The current carbon storage in the Taihang Mountains ranges from 1472.91 × 106 t to 1478.17 × 106 t (t is the abbreviation of ton), and shows a decreasing trend, which is due to the decrease in forest and the increase in construction land. (3) Slope and Normalized Difference Vegetation Index (NDVI) are the main driving factors affecting the spatial variation of carbon storage in the Taihang Mountains ecosystem. Temperature, precipitation, and population density are the secondary factors affecting the spatial variation of carbon storage. (4) The synergy between the driving factors is more potent than the individual factor, which is the most evident between NDVI and slope. This means some areas may have more abundant carbon storage under the combined effect of slope and NDVI.
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